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I simply can't for the love of god do a simple damn conversion and it's been bugging me over the last weekend: https://en.wikipedia.org/wiki/Rg_chromaticity

Can anyone help out with an efficient way to do this in OpenCV? I keep getting black images, here's my current attempt:

for (int i = 0; i < mRgb.cols; i++) {
        for (int j = 0; j < mRgb.rows; j++) {

            Vec3b intensity = mRgb.at<Vec3b>(j, i);
            float b = intensity.val[0];
            float g = intensity.val[1];
            float r = intensity.val[2]; 

            float rgbSum = (float) (r + g + b);
            float redNorm = (float) (r / rgbSum);
            float greenNorm = (float) (g / rgbSum);
            float blueNorm = (float) (b / rgbSum); 

            res.at<Vec3b>(j, i)[0] = blueNorm;
            res.at<Vec3b>(j, i)[1] = greenNorm;
            res.at<Vec3b>(j, i)[2] = redNorm;
        }
    }

I am positive this can be done more efficiently, please help me out. Isn't there a way in which you can split the channels, divide them to the channel sum and then add up the 3 new channels into a new image? In matlab it's done like this: http://stackoverflow.com/questions/21712828/convert-image-to-a-chromaticity-image

Regards!

I simply can't for the love of god do a simple damn conversion and it's been bugging me over the last weekend: https://en.wikipedia.org/wiki/Rg_chromaticity

Can anyone help out with an efficient way to do this in OpenCV? I keep getting black images, here's my current attempt:

for (int i = 0; i < mRgb.cols; i++) {
        for (int j = 0; j < mRgb.rows; j++) {

            Vec3b intensity = mRgb.at<Vec3b>(j, i);
            float b = intensity.val[0];
            float g = intensity.val[1];
            float r = intensity.val[2]; 

            float rgbSum = (float) (r + g + b);
            float redNorm = (float) (r / rgbSum);
            float greenNorm = (float) (g / rgbSum);
            float blueNorm = (float) (b / rgbSum); 

            res.at<Vec3b>(j, i)[0] = blueNorm;
            res.at<Vec3b>(j, i)[1] = greenNorm;
            res.at<Vec3b>(j, i)[2] = redNorm;
        }
    }

I am positive this can be done more efficiently, please help me out. out.

Isn't there a way in which you can split the channels, divide them to the channel sum and then add up the 3 new channels into a new image? In matlab it's done like this: http://stackoverflow.com/questions/21712828/convert-image-to-a-chromaticity-image

Regards!

I simply can't for the love of god do a simple damn conversion and it's been bugging me over the last weekend: https://en.wikipedia.org/wiki/Rg_chromaticity

Can anyone help out with an efficient way to do this in OpenCV? I keep getting black images, here's my current attempt:

for (int i = 0; i < mRgb.cols; i++) {
        for (int j = 0; j < mRgb.rows; j++) {

            Vec3b intensity = mRgb.at<Vec3b>(j, i);
            float b = intensity.val[0];
            float g = intensity.val[1];
            float r = intensity.val[2]; 

            float rgbSum = (float) (r + g + b);
            float redNorm = (float) (r / rgbSum);
            float greenNorm = (float) (g / rgbSum);
            float blueNorm = (float) (b / rgbSum); 

            res.at<Vec3b>(j, i)[0] = blueNorm;
            res.at<Vec3b>(j, i)[1] = greenNorm;
            res.at<Vec3b>(j, i)[2] = redNorm;
        }
    }

I am positive this can be done more efficiently, please help me out.

Isn't there a way in which you can split the channels, divide them to the channel sum and then add up the 3 new channels into a new image? In matlab it's done like this: http://stackoverflow.com/questions/21712828/convert-image-to-a-chromaticity-image

Edit, here's a second attempt using channels:

    Mat channel[3];
    split(mRgb, channel);

    double alpha = 1.0;
    double beta = 1.0;
    Mat tmpSum;
    Mat rgbSum;
    addWeighted(channel[0], alpha, channel[1], beta, 0.0, tmpSum);
    addWeighted(tmpSum, alpha, channel[2], beta, 0.0, rgbSum);

    Mat newR;
    Mat newG;
    Mat newB;
    divide(channel[0], rgbSum, newR);
    divide(channel[1], rgbSum, newG);
    divide(channel[2], rgbSum, newB);

    Mat in[] = { newB, newG, newR };
    int from_to[] = { 0, 0, 1, 1, 2, 2 };
    mixChannels(in, 3, &mRgb, 1, from_to, 3);

I get the famous fatal signal 11 on Android using OpenCV in ndk.

Regards!

I simply can't for the love of god do a simple damn conversion and it's been bugging me over the last weekend: https://en.wikipedia.org/wiki/Rg_chromaticity

Can anyone help out with an efficient way to do this in OpenCV? I keep getting black images, here's my current attempt:

for (int i = 0; i < mRgb.cols; i++) {
        for (int j = 0; j < mRgb.rows; j++) {

            Vec3b intensity = mRgb.at<Vec3b>(j, i);
            float b = intensity.val[0];
            float g = intensity.val[1];
            float r = intensity.val[2]; 

            float rgbSum = (float) (r + g + b);
            float redNorm = (float) (r / rgbSum);
            float greenNorm = (float) (g / rgbSum);
            float blueNorm = (float) (b / rgbSum); 

            res.at<Vec3b>(j, i)[0] = blueNorm;
            res.at<Vec3b>(j, i)[1] = greenNorm;
            res.at<Vec3b>(j, i)[2] = redNorm;
        }
    }

I am positive this can be done more efficiently, please help me out.

Isn't there a way in which you can split the channels, divide them to the channel sum and then add up the 3 new channels into a new image? In matlab it's done like this: http://stackoverflow.com/questions/21712828/convert-image-to-a-chromaticity-image

Edit, here's a second attempt using channels:

    Mat channel[3];
    split(mRgb, channel);

    double alpha = 1.0;
    double beta = 1.0;
    Mat tmpSum;
    Mat rgbSum;
    addWeighted(channel[0], alpha, channel[1], beta, 0.0, tmpSum);
    addWeighted(tmpSum, alpha, channel[2], beta, 0.0, rgbSum);

    Mat newR;
    Mat newG;
    Mat newB;
    divide(channel[0], rgbSum, newR);
newB);
    divide(channel[1], rgbSum, newG);
    divide(channel[2], rgbSum, newB);
newR);

    Mat in[] = { newB, newG, newR };
    int from_to[] = { 0, 0, 1, 1, 2, 2 };
    mixChannels(in, 3, &mRgb, 1, from_to, 3);

I get the famous fatal signal 11 on Android using OpenCV in ndk.

Regards!

I simply can't for the love of god do a simple damn conversion and it's been bugging me over the last weekend: https://en.wikipedia.org/wiki/Rg_chromaticity

Can anyone help out with an efficient way to do this in OpenCV? I keep getting black images, here's my current attempt:

for (int i = 0; i < mRgb.cols; i++) {
        for (int j = 0; j < mRgb.rows; j++) {

            Vec3b intensity = mRgb.at<Vec3b>(j, i);
            float b = intensity.val[0];
            float g = intensity.val[1];
            float r = intensity.val[2]; 

            float rgbSum = (float) (r + g + b);
            float redNorm = (float) (r / rgbSum);
            float greenNorm = (float) (g / rgbSum);
            float blueNorm = (float) (b / rgbSum); 

            res.at<Vec3b>(j, i)[0] = blueNorm;
            res.at<Vec3b>(j, i)[1] = greenNorm;
            res.at<Vec3b>(j, i)[2] = redNorm;
        }
    }

I am positive this can be done more efficiently, please help me out.

Isn't there a way in which you can split the channels, divide them to the channel sum and then add up the 3 new channels into a new image? In matlab it's done like this: http://stackoverflow.com/questions/21712828/convert-image-to-a-chromaticity-image

Edit, here's a second attempt using channels:

    Mat channel[3];
    split(mRgb, channel);

    double alpha = 1.0;
    double beta = 1.0;
    Mat tmpSum;
    Mat rgbSum;
    addWeighted(channel[0], alpha, channel[1], beta, 0.0, tmpSum);
    addWeighted(tmpSum, alpha, channel[2], beta, 0.0, rgbSum);

    Mat newR;
    Mat newG;
    Mat newB;
    divide(channel[0], rgbSum, newB);
    divide(channel[1], rgbSum, newG);
    divide(channel[2], rgbSum, newR);

    Mat in[] = { newB, newG, newR };
    int from_to[] = { 0, 0, 1, 1, 2, 2 };
    mixChannels(in, 3, &mRgb, 1, from_to, 3);

I get the famous fatal signal 11 on Android using OpenCV in ndk.

Regards!